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The Use and Influence of Indicators in Decisions About Technological Innovation

Quantitative Results from Questionnaires in Portugal

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Scientific Knowledge and the Transgression of Boundaries

Abstract

This work focuses on the role that indicators play in decisions about technological innovation in three knowledge-intensive groups: Researchers in public institutions, leaders in research and development in industry, and policy-makers. It presents quantitative results obtained from three questionnaires sent to these groups in Portugal. The study suggests that indicators are instruments in decisions about technological innovation but that their influence depends on the social context and the type of decision. Results show that indicators were used in all the groups but were not significantly influential in decisions on technological innovation . Researchers were more influenced by indicators than by their social relations, revealing a balance between an instrumental use, a symbolic use and no use at all of indicators. Those in this group focused their decisions on the acquisition of products or technology, and identified its main influence as being in the sources and users of knowledge. The majority of the business and policy-makers revealed that indicators were mostly used in a symbolic way, and that they were more influenced by social relations than by indicators. Those in the business group focused their decisions on the development of products or technology, and declared that hierarchies and users exerted a stronger influence on their decisions. The policy-makers focused their decisions on the design of innovation policies, and they too were influenced more strongly by hierarchies and knowledge sources.

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Notes

  1. 1.

    See, for example, the prolific data-gathering work about the world economy of Maddison (2001, 2003).

  2. 2.

    In order to simplify the presentation of the data, the researchers group will be briefly denominated as researchers, and the group of business R&D &I personnel as “business” or “companies”.

  3. 3.

    The Swedish General Decision-making Style (GDMS) inventory was created on the basis of work by (Scott and Bruce 1995) and validated with 1441 male military officers with regard to career decision-making and, later, with samples of students, engineers, and technicians with regard to important decisions in general.

  4. 4.

    The term “stakeholders” is here referred to sensu lato as defined by Freeman and Reed (1983), which includes shareowners, employees, customers, suppliers, lenders, and society.

  5. 5.

    In this emotional-intuitive model, the use of indicators is rather limited because it is normally based on qualitative methods.

  6. 6.

    Some researchers consider the scientific study of intuition impossible, seeing it as an esoteric phenomenon or just erratic nonsense. However, Schoemaker and Russo (1993) considered that intuition can be brilliant when based on extensive learning from past experience, probably reflecting an automated expertise. In fact, the current technical conception of intuition implies that it arises from knowledge and experience. It also implies that intuition involves a form of information processing that might be more implicit than explicit, but which is not at all irrational.

  7. 7.

    According to Policastro (1999), intuition may be defined as a tacit form of knowledge that guides decision-making in a promising direction, which in the context of innovation leads to potentially creative results. Intuition is assumed to be especially important in tasks with high complexity, short time horizons, ill-structured problems, and involving moral evaluations (Linn et al. 2013). It involves the ability to quickly synthesize and integrate information and use of decision-makers’ experience. To Policastro, intuition seems to be most useful when there are high stakes, a high level of uncertainty , and pressure to make the right decision in a limited amount of time. In her perspective, intuitions are not infallible, since they are like rough estimates, which necessarily entail some margin of error. In addition, research showed that there is not much knowledge about how intuition works, under which circumstances it may or may not be useful, or how to reduce its margin of error.

  8. 8.

    Editors’ note: The author calls himself “6”, but “Six” is used here in order to avoid confusion.

  9. 9.

    In fact, although commonly accepted, debates continue about the object measured by indicators (such as non-R&D innovation expenditure, SMEs introducing marketing/organizational innovations, and innovative SMEs collaborating with others), as well as the nature of reality being measured. Furthermore, an indicator’s claim to objectivity has to be restrained to “knowledge produced in conformity with the prevailing standards of scientific practice as determined by the current judgements of the scientific community ” (Porter 1995, p. 216).

  10. 10.

    Intelligence quotient, commonly known as IQ, is a score derived from standardized tests designed to assess intelligence.

  11. 11.

    According to Linstone (2008), the Rand Corporation is the most influential American think tank of the second half of the twentieth century. The company was established in 1946 to deal with useful applications labelled “operations research”, which applied mathematics to problems such as interceptor vectoring and convoy protection. Presently, Rand is well accepted in decision-making corridors of Washington, DC, and offers vast research and analysis to the US armed forces. Rand is currently financed by the US government, private foundations, corporations (including the health care industry), universities, and private individuals.

  12. 12.

    (i) Rules of thumb—for example, if the rule is: If there is a 20 % increase in profits we need to buy new technology; (ii) evidence-based analysis—for example, when making cost-benefit analyses, weighting options, etc.; and (iii) operations research—using mathematical models to explore quantified evidences. All three methods require some sort of quantification , which would mean having an indicator of something.

  13. 13.

    Purchasing Power Parities (national currency per dollar).

  14. 14.

    A deoxyribonucleic acid (DNA ) sequencer is a scientific instrument used to automate the DNA sequencing process. A sequencer is used to automatically determine the order of the DNA ’s four constituents: Adenine, guanine, cytosine, and thymine.

  15. 15.

    According to the Oxford Dictionaries Online (2014), complexity means the state or quality of being intricate or complicated.

  16. 16.

    See also Böhle (2011), Jalonen and Lehtonen (2011), Fusari and Reati (2013), and Meijer et al. (2007).

  17. 17.

    But many more can be identified. For example, Carbonell and Rodríguez-Escudero (2009) considered only two aspects of uncertainty: Technology novelty and technological turbulence. In their study of innovation on biomass gasification projects in the Netherlands, Meijer et al. (2007) reported that technological, political and resource uncertainty are the most dominant sources of perceived uncertainty influencing entrepreneurial decision-making.

  18. 18.

    The National R&D Survey (named IPCTN) is a reliable long-term survey that captures in detail data about any existing R&D projects and about researchers and companies involved in R&D in Portugal. The survey has also internationally comparable standards, is based on the Frascati Manual, and is regularly checked by the OECD, Eurostat, and the National Institute of Statistics.

  19. 19.

    The 2010 National R&D Survey database detected 59 companies in the country that met the criteria. The business questionnaire was sent, however, only to 57 due to the closure of two firms.

  20. 20.

    The two sampling techniques allowed the identification of 65 policy-makers but, after a significant number of attempts to locate the policy-makers, six of them were considered to be unreachable.

  21. 21.

    Only results related to “Very important” classification are shown in the table.

  22. 22.

    The results were adjusted to take account of responses to the question of decision-making styles, which is not presented here.

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Acknowledgments

The author gratefully acknowledges the financial support of the Portuguese Fundação para a Ciência e a Tecnologia (Ref: SFRH/BD/76200/2011), and the comments of the editors on previous versions of this article.

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  1. Karlsruhe, Germany

    Nuno Boavida

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  1. Nuno Boavida
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Correspondence to Nuno Boavida .

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  1. Karlsruher Institut für Technologie (KIT) , Karlsruhe, Germany

    Bettina-Johanna Krings

  2. University of the Basque Country UPV/EHU , Vitoria-Gasteiz, Spain

    Hannot Rodríguez

  3. Berlin, Germany

    Anna Schleisiek

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Boavida, N. (2016). The Use and Influence of Indicators in Decisions About Technological Innovation. In: Krings, BJ., Rodríguez, H., Schleisiek, A. (eds) Scientific Knowledge and the Transgression of Boundaries. Technikzukünfte, Wissenschaft und Gesellschaft / Futures of Technology, Science and Society. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-14449-4_4

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